Context Of Software-Defined Networking Research Articles

Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security

The research addresses the critical anomaly detection problem in Software-Defined Networking (SDN), a domain where network integrity and security are paramount. Employing the Isolation Forest algorithm, a machine learning model renowned for its efficacy in identifying outliers, the study systematically generates synthetic network traffic data to train and test the model's detection capabilities. The methodology encompasses simulating a range of contamination rates to reflect varying degrees of anomalous activities within the network. Key findings indicate that while the model exhibits potential in anomaly detection, as reflected by the progressive increase in triggered alerts and policy changes, its performance metrics, such as precision, recall, F1-score, and AUC, reveal limitations in its current application. The research contributes to the field by providing a detailed analysis of the Isolation Forest algorithm's performance in an SDN context and laying the groundwork for future enhancements in machine learning-based security measures within these networks.

Analytical study of effects of DDoS mitigation methods

Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructure and services. Software Defined Networks (SDNs) have emerged as a promising technology to enhance network flexibility and adaptability. This paper explores various DDoS attack mitigation approaches within the context of SDNs, with a focus on their effectiveness, limitations, and key considerations. The study begins by providing an overview of DDoS attacks and their evolving complexities. It then delves into the fundamental principles of SDNs and their potential advantages in combating DDoS attacks. Various mitigation techniques are analyzed, including rate limiting, flow diversion, and traffic anomaly detection, and their applicability in an SDN environment is discussed Our research takes a multi-dimensional approach, evaluating the performance of mitigation methods in terms of attack detection, response time, false positives, and the impact on legitimate traffic. Additionally, the study considers the adaptability of these methods to emerging threats, such as IoT-based attacks and zero-day vulnerabilities. Furthermore, we discuss the trade-offs and challenges associated with the implementation of these methods, including their impact on user experience, resource utilization, and operational overhead. The results of this comparative analysis aim to provide valuable insights for organizations and network security professionals in selecting appropriate DDoS mitigation strategies. This paper provides insights into the evolving landscape of DDoS attack mitigation in Software Defined Networks, emphasizing the need for a proactive and dynamic defence strategy to protect network infrastructure and ensure uninterrupted service availability. This study serves as a valuable resource for mitigating the risks and consequences of DDoS attacks in today's interconnected and digitally-dependent world.

A lightweight DDoS detection scheme under SDN context

Software-defined networking (SDN), a novel network paradigm, separates the control plane and data plane into different network equipment to realize the flexible control of network traffic. Its excellent programmability and global view present many new opportunities. DDoS detection under the SDN context is an important and challenging research field. Some previous works attempted to collect and analyze statistics related to flows, usually recorded in switches, to address DDoS threats. In contrast, other works applied machine learning-based solutions to identify DDoS and achieved promising results. Generally, most previous works need to periodically request flow rules or packets to obtain flow statistics or features to detect stealthy exceptions. Nevertheless, the request for flow rules is very time-consuming and CPU-consuming; moreover may congest the communication channel between the controller and the switches. Therefore, we present FORT, a lightweight DDoS detection scheme, which spreads the rule-based detection algorithm at edge switches and determines whether to start it by periodically retrieving the ports state. A time-series algorithm, ARIMA, is utilized to determine the port statistics adaptively, and an SVM algorithm is applied to detect whether a DDoS attack does occur. Representative experiments demonstrate that FORT can significantly reduce the controller load and provide a reliable detection accuracy. Referring to the false alarm rate of 1.24% in the comparison scheme, the false alarm rate of this scheme is only 0.039%, which significantly reduces the probability of false alarm. Besides, by introducing the alarm mechanism, this scheme can reduce the load of the southbound channel by more than 60% in the normal state.

An Extendable Software Architecture for Mitigating ARP Spoofing-Based Attacks in SDN Data Plane Layer

Software-defined networking (SDN) is an emerging network architecture that brings benefits in network function virtualization, performance, and scalability. However, the scalability feature also increases the number of possible vulnerabilities through multiple entry points in the network. Address Resolution Protocol (ARP) spoofing-based attacks are widely encountered and allow an attacker to assume the identity of a different computer, facilitating other attacks, such as Man in the Middle (MitM). In the SDN context, most solutions employ a controller to detect and mitigate attacks. However, interacting with the control plane involves asynchronous network communication, which causes delayed responses to an attack. The current work avoids these delays by being implemented solely in the data plane through extendable and customizable software architecture. Therefore, faster response times improve network reliability by automatically blocking attackers. As attacks can be generated with a variety of tools and in networks experiencing different traffic patterns, the current solution is created to allow flexibility and extensibility, which can be adapted depending on the running environment. Experiments were run performing ARP spoofing-based attacks using KaliLinux, Mininet, and OpenVSwitch. The presented results are based on traffic pattern analysis offering greater customization capabilities and insight compared to similar work in this area.

Early DDoS Detection and Prevention with Traced-Back Blocking in SDN Environment

The flow of information is a valuable asset for every company and its consumers, and Distributed Denial-of-Service (DDoS) assaults pose a substantial danger to this flow. If we do not secure security, hackers may steal information flowing across a network, posing a danger to a business and society. As a result, the most effective ways are necessary to deal with the dangers. A DDoS attack is a well-known network infrastructure assault that prevents servers from servicing genuine customers. It is necessary to identify and block a DDoS assault before it reaches the server in order to avoid being refused services. This prompted us to develop a unique way for detecting and preventing DDoS attacks at the router level in a Software-Defined Network (SDN) environment. This study demonstrates how the method efficiently integrates the first and second signatures in SDN infrastructure domains to identify and prevent DDoS attacks. It also proposes an Early DDoS Detection and Prevention (EDDDeP)-based approach for detecting and blocking malicious traffic in an SDN context. This article covers the EDDDeP, which assists in identifying and preventing DDoS in SDN to prevent malicious traffic from reaching its intended target. As a consequence, the DDoS assault is ultimately contained inside the environment, eliminating superfluous traffic in the DDoS network architecture. This method offers a unique technique to detect a DDoS assault and notify nearby neighbours in order to avert server damage.

MitM detection and defense mechanism CBNA-RF based on machine learning for large-scale SDN context

Software defined network (SDN) is a promising new network abstraction that aims to improve and facilitate network management. Due to its centralized architecture and the lack of intelligence on the data plane, SDN suffers from many security issues that slows down its deployment. Man in the Middle (MitM) attack is considered as one of the most devastating attacks in an SDN context. In fact, MitM attack allows the attackers to capture, duplicate and spoof flows by targeting southbound interfaces and SDN nodes. Furthermore, it’s very difficult to detect MitM attacks since it is performed passively at the SDN level. To reduce the impact of this attack, we generally set up security policies and authentication mechanisms. However, these techniques are not applicable for a large scale SDN architecture as they require complexes and static configurations and as they negatively influence on network performance. In this paper, we propose an intrusion detection and prevention framework by using machine learning techniques to detect and stop MitM attempts. To do so, we build a context-based node acceptance based on the random forest model (CBNA-RF), which helps to setting-up appropriate security policies and to automating defense operations on a large-scale SDN context. This mechanism can realize a quick and early detection of MitM attacks by automatically detecting malicious nodes without affecting performances. The evaluation of the proposed framework demonstrates that our model can correctly classify and detect malicious connections and nodes while keeping high accuracy and precision scores.

A High-Speed, Scalable, and Programmable Traffic Manager Architecture for Flow-Based Networking

In this paper, we present a programmable and scalable traffic manager (TM) architecture, targeting requirements of high-speed networking devices, especially in the software-defined networking context. This TM is intended to ease the deployability of new architectures through field-programmable gate array (FPGA) platforms and to make the data plane programmable and scalable. Flow-based networking allows treating traffic in terms of flows rather than as a simple aggregation of individual packets, which simplifies scheduling and bandwidth allocation for each flow. Programmability brings agility, flexibility, and rapid adaptation to changes, allowing to meet network requirements in real-time. Traffic management with fast queuing and reduced latency plays an important role to support the upcoming 5G cellular communication technology. The proposed TM architecture is coded in C++ and is synthesized with the Vivado High-Level Synthesis tool. This TM is capable of supporting links operating beyond 40 Gb/s, on the ZC706 board and XCVU440-FLGB2377-3-E FPGA device from Xilinx, while achieving 80 Gb/s and 100 Gb/s throughput, respectively. The resulting placed and routed design was tested on the ZC706 board with its embedded ARM processor controlling table updates.

Defending against Packet-In messages flooding attack under SDN context

Software-defined networking (SDN) is the key outcome of extensive research efforts over the past few decades toward transforming the Internet to a more programmable, configurable, and manageable infrastructure. At the same time, SDN will surely become a new target of cyber attackers. In this paper, we point out one of the critical vulnerabilities in SDNs, the capacity of controller, which is most likely to be attacked. Due to the logical centralized management, the breakdown of a controller may disrupt a whole SDN network, which can be easily occurred by Packet-In messages flooding attack (a network-level DDoS attack). To provide a robust environment in SDN, we propose an effective detection method, which has low overhead and high accuracy. We first classify the potential switches that are compromised using Bayesian Network, which is a supervised learning algorithm. Then, we deploy the anomaly detection on the vulnerable switches to detect the Packet-In messages flooding attack based on fuzzy c-means. Extensive simulations and testbed-based experiments show that the proposed solution can defeat the Packet-In messages flooding attack with low overhead and high accuracy.

On reliability-optimized controller placement for Software-Defined Networks

By decoupling control plane and data plane, Software-Defined Networking (SDN) approach s i m p l i f i e s network management and speeds up network innovations. These benefits have led not only to prototypes, but also real SDN deployments. For wide-area SDN deployments, multiple controllers are often required, and the placement of these controllers becomes a particularly important task in the SDN context. This paper studies the problem of placing controllers in SDNs, so as to maximize the reliability of SDN control networks. We present a novel metric, called expected percentage of control path loss, to characterize the reliability of SDN control networks. We formulate the reliability-aware control placement problem, prove its NP-hardness, and examine several placement algorithms that can solve this problem. Through extensive simulations using real topologies, we show how the number of controllers and their placement influence the reliability of SDN control networks. Besides, we also found that, through strategic controller placement, the reliability of SDN control networks can be significantly improved without introducing unacceptable switch-to-controller latencies.